An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.
This page lists names, links and short descriptions. The original list on GitHub is the source and belongs to its authors.
is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices. It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained…
MLIR-based and enables conversion of new classes of models such as Mask R-CNN and Mobile BERT etc., supports functional control flow and better error handling during conversion. Enabled by default in the nightly builds.
Makes mobile development easier (Android sample code).
Create your custom image & text classification models easily in a few lines of code. See below the Icon Classifier for a tutorial by the community.
It is finally here! Currently limited to transfer learning for image classification only but it's a great start. See the official Android sample code and another one from the community (Blog | Android).
How to use the Hexagon Delegate to speed up model inference on mobile and edge devices. Also see blog post Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs.
Provides a standard for model descriptions which also enables Code Gen and Android Studio ML Model Binding.
With official Android and iOS examples.
Quantized and floating point variants.
Set "Model format = TFLite" to find TensorFlow Lite models.
Checkout this repo for sample app ideas and seeking help for your tutorial projects. Once a project gets completed, the links of the TensorFlow Lite model(s), sample code and tutorial will be added to this awesome list.
is a mobile SDK that brings Google's ML expertise to mobile developers.
A tutorial with material design Android (Kotlin) sample - recognize, identify Language and translate text from live camera with ML Kit for Firebase.
A blog post with a Flutter sample code.
A talk with Android (Kotlin) sample code.
Created by @EdgeImpulse to help you to train TensorFlow Lite models for embedded devices in the cloud.
A cross platform (mobile, desktop and Edge TPUs) AI pipeline by Google AI. (PM Ming Yong) | MediaPipe examples.
Edge hardware by Google. Coral Edge TPU examples.
Provides a dart API similar to the TensorFlow Lite Java API for accessing TensorFlow Lite interpreter and performing inference in flutter apps. tflite_flutter on pub.dev.
A website for benchmarking computer vision models on smartphones.
How to measure model performance on Android and iOS.
How to design machine learning powered features. A good example: ML Kit Showcase App.
Learn how to design human-centered AI products.
A repository showing non-trivial conversion processes and general explorations in TensorFlow Lite.
An Android-based app to profile TensorFlow Lite models and measure its performance on smartphone.
A repository refactors and rewrites all the TensorFlow Lite Android examples which are included in the TensorFlow official website.
A collection of Tensorflow Lite Android example Apps in Kotlin, to show different kinds of kotlin implementation of the example apps
By Nitin Tiwari.
By Khanh LeViet.
By Khanh LeViet and Luiz Gustavo Martins.
By Khanh LeViet.
By Brandon Satrom.
By the TensorFlow team.
By Sara Robinson, Aakanksha Chowdhery, and Jonathan Huang.
By Pete Warden.
) - By Laurence Moroney.
(early access) - By Laurence Moroney (@lmoroney).
By Laurence Moroney (@lmoroney).
Build scalable real-world projects to implement end-to-end neural networks on Android and iOS (GitHub) - By Anubhav Singh (@xprilion) and Rimjhim Bhadani (@Rimjhim28).
By Pete Warden (@petewarden) and Daniel Situnayake (@dansitu).
By Anirudh Koul (@AnirudhKoul), Siddha Ganju (@SiddhaGanju), and Meher Kasam (@MeherKasam).
(Hacktoberfest 2021)
(GDG Kolkata meetup).
(TF Dev Summit 2020).
(TF Dev Summit 2020).
Udacity course by Daniel Situnayake (@dansitu), Paige Bailey (@DynamicWebPaige), and Juan Delgado.
Coursera course by Laurence Moroney (@lmoroney).
A series of edX courses created by Harvard in collaboration with Google. Instructors - Vijay Janapa Reddi, Laurence Moroney, and Pete Warden.
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